Martins Akugbe Arasomwan

TL;DR: An experiment was conducted to acquire a percentage value of the search space limits to compute the particle velocity limits in LDIW-PSO based on commonly used benchmark global optimization problems, and five well-known benchmark optimization problems were used to show the outstanding performance of LDIO over some of its competitors which have in the past claimed superiority over it.

TL;DR: This paper proposes a new PSO variant called Adaptive Velocity PSO (AV-PSO), which adaptively adjusts the velocity of particles based on Euclidean distance between the position of each particle and the positions of the global best particle.

TL;DR: A new local search technique is proposed and used to improve the performance of particle swarm optimization algorithms by addressing the problem of premature convergence and demonstrates better convergence velocity and precision, stability, robustness, and global-local search ability than the competing variants.

TL;DR: This paper establishes that the basic particle swarm optimisation BPSO technique can perform efficiently without some or any of the control parameters in the particle velocity update formula and presents a modified B PSO M-BPSO that ameliorate the problem of premature convergence associated with PSO when optimising high dimensional multi-modal problems.

TL;DR: The efficiency of the proposed algorithm for BAP with no blood units wasted and very low importation, where necessary, from outside the blood bank can serve as a benchmark and basis for decision support tools for real-life deployment.